Google Professional Data Engineer Exam Latest Update With 100%
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Your company built a TensorFlow neutral-network model with a large number of neurons
and layers. The model fits well for the training data. However, when tested against new
data, it performs poorly. What method can you employ to address this? - Answer
Dropout Methods
Dropout is a regularization technique commonly used in neural networks to prevent
overfitting. It helps improve the generalization of the model by randomly setting a
fraction of the neurons to zero during each training iteration, which prevents the
network from relying too heavily on specific neurons. This, in turn, can lead to better
performance on new, unseen data.
You are building a model to make clothing recommendations. You know a user's fashion
preference is likely to change over time, so you build a data pipeline to stream new data
back to the model as it becomes available. How should you use this data to train the
model? - Answer Continuously retrain the model on a combination of existing data and
the new data.
This approach allows the model to benefit from both the historical data (existing data)
and the new data, ensuring that it adapts to changing preferences while retaining
knowledge from the past. By combining both types of data, the model can learn to make
recommendations that are up-to-date and relevant to users' evolving preferences.
You designed a database for patient records as a pilot project to cover a few hundred
patients in three clinics. Your design used a single database table to represent all
patients and their visits, and you used self-joins to generate reports. The server
resource utilization was at 50%. Since then, the scope of the project has expanded. The
database must now store 100 times more patient records. You can no longer run the
reports, because they either take too long or they encounter errors with insufficient
compute resources. How should you adjust the database design? - Answer Normalize
the master patient-record table into the patient table and the visits table, and create
other necessary tables to avoid self-join.
,Normalization is a technique used to organize data in a relational database to reduce
data redundancy and improve data integrity. Breaking the patient records into separate
tables (patient and visits) and eliminating self-joins will make the database more
scalable and improve query performance. It also helps maintain data integrity and
makes it easier to manage large datasets efficiently.
You create an important report for your large team in Google Data Studio 360. The
report uses Google BigQuery as its data source. You notice that visualizations are not
showing data that is less than 1 hour old. What should you do? - Answer Disable caching
by editing the report settings.
By default, Google Data Studio 360 caches data to improve performance and reduce the
amount of queries made to the data source. However, this can cause visualizations to
not show data that is less than 1 hour old, as the cached data is not up-to-date. To
resolve this, you should disable caching by editing the report settings.
An external customer provides you with a daily dump of data from their database. The
data flows into Google Cloud Storage GCS as comma-separated values(CSV) files. You
want to analyze this data in Google BigQuery, but the data could have rows that are
formatted incorrectly or corrupted. How should you build this pipeline? - Answer Run a
Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors
to another dead-letter table for analysis.
Google Cloud Dataflow allows you to create a data pipeline that can preprocess and
transform data before loading it into BigQuery. This approach will enable you to handle
problematic rows, push them to a dead-letter table for later analysis, and load the valid
data into BigQuery.
Your weather app queries a database every 15 minutes to get the current temperature.
The frontend is powered by Google App Engine and server millions of users. How should
you design the frontend to respond to a database failure? - Answer Retry the query with
exponential backoff, up to a cap of 15 minutes.
If your application attempts to connect to the database and does not succeed, the
database could be temporarily unavailable. In this case, sending too many simultaneous
connection requests might waste additional database resources and increase the time
needed to recover. Using exponential backoff prevents your application from sending
,an unresponsive number of connection requests when it can't connect to the database.
You are creating a model to predict housing prices. Due to budget constraints, you must
run it on a single resource-constrained virtual machine. Which learning algorithm should
you use? - Answer Linear regression
To decide when a liner regression should be used or logistics regression needs to be
used: If you are forecasting that is the values in the column that you are predicting is
numeric, it is always liner regression. If you are classifying, that is buy or no buy, yes or
no, you will be using logistics regression.
You are building new real-time data warehouse for your company and will use Google
BigQuery streaming inserts. There is no guarantee that data will only be sent in once but
you do have a unique ID for each row of data and an event timestamp. You want to
ensure that duplicates are not included while interactively querying data. Which query
type should you use? - Answer Use the ROW_NUMBER window function with PARTITION
by unique ID along with WHERE row equals 1.
This approach will assign a row number to each row within a unique ID partition, and by
selecting only rows with a row number of 1, you will ensure that duplicates are excluded
in your query results. It allows you to filter out redundant rows while retaining the latest
or earliest records based on your timestamp column.
Your company is using WILDCARD tables to query data across multiple tables with
similar names. The SQL statement is currently failing with the following error: #Syntax
Error: Expected end of statement but got "-" at [4:11}
SELECT Age FROM bigquery-public-data.noaa_gsod.gsod WHERE age != 99
AND_TABLE_SUFFIX = '1929' ORDER BY age DESC; - Answer
`bigquery-public-data.noaa_gsod.gsod*`
The wildcard table name contains the special character (*), which means that you must
enclose the wildcard table name in backtick (`) characters.
Your company is in a highly regulated industry. One of your requirements is to ensure
individual users have access only to the minimum amount of information required to do
, their jobs. You want to enforce this requirement with Google BigQuery. Which three
approaches can you take? (Choose three.) - Answer 1) Restrict access to tables by role
2) Restrict BigQuery API access to approved users
3) Segregate data across multiple tables or databases
Restrict access to tables by role: You can use BigQuery's access controls to restrict
access to specific tables based on user roles. This allows you to ensure that users can
only access the data they need to do their job. Restrict BigQuery API access to
approved users: By using Cloud Identity and Access Management (IAM) you can control
who has access to the BigQuery API, and what actions they are allowed to perform. This
will help to ensure that only authorized users can access the data. Segregate data
across multiple tables or databases: You can use multiple tables or databases to
separate different types of data, so that users can only access the data they need. This
will prevent users from seeing data they shouldn't have access to.
You are designing a basket abandonment system for an ecommerce company. The
system will send a message to a user based on these rules:
✑ No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
✑ Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be
sent. How should you design the pipeline? - Answer Use a session window with a gap
time duration of 60 minutes
Your company handles data processing for a number of different clients. Each client
prefers to use their own suite of analytics tools, with some allowing direct query access
via Google BigQuery. You need to secure the data so that clients cannot see each
other's data. You want to ensure appropriate access to the data.Which three steps
should you take? (Choose three.) - Answer 1) Load data into a different dataset for each
client.
2) Restrict a client's dataset to approved users.
3) Use the appropriate identity and access management (IAM) roles for each client's
users
Verified Correct Information
Your company built a TensorFlow neutral-network model with a large number of neurons
and layers. The model fits well for the training data. However, when tested against new
data, it performs poorly. What method can you employ to address this? - Answer
Dropout Methods
Dropout is a regularization technique commonly used in neural networks to prevent
overfitting. It helps improve the generalization of the model by randomly setting a
fraction of the neurons to zero during each training iteration, which prevents the
network from relying too heavily on specific neurons. This, in turn, can lead to better
performance on new, unseen data.
You are building a model to make clothing recommendations. You know a user's fashion
preference is likely to change over time, so you build a data pipeline to stream new data
back to the model as it becomes available. How should you use this data to train the
model? - Answer Continuously retrain the model on a combination of existing data and
the new data.
This approach allows the model to benefit from both the historical data (existing data)
and the new data, ensuring that it adapts to changing preferences while retaining
knowledge from the past. By combining both types of data, the model can learn to make
recommendations that are up-to-date and relevant to users' evolving preferences.
You designed a database for patient records as a pilot project to cover a few hundred
patients in three clinics. Your design used a single database table to represent all
patients and their visits, and you used self-joins to generate reports. The server
resource utilization was at 50%. Since then, the scope of the project has expanded. The
database must now store 100 times more patient records. You can no longer run the
reports, because they either take too long or they encounter errors with insufficient
compute resources. How should you adjust the database design? - Answer Normalize
the master patient-record table into the patient table and the visits table, and create
other necessary tables to avoid self-join.
,Normalization is a technique used to organize data in a relational database to reduce
data redundancy and improve data integrity. Breaking the patient records into separate
tables (patient and visits) and eliminating self-joins will make the database more
scalable and improve query performance. It also helps maintain data integrity and
makes it easier to manage large datasets efficiently.
You create an important report for your large team in Google Data Studio 360. The
report uses Google BigQuery as its data source. You notice that visualizations are not
showing data that is less than 1 hour old. What should you do? - Answer Disable caching
by editing the report settings.
By default, Google Data Studio 360 caches data to improve performance and reduce the
amount of queries made to the data source. However, this can cause visualizations to
not show data that is less than 1 hour old, as the cached data is not up-to-date. To
resolve this, you should disable caching by editing the report settings.
An external customer provides you with a daily dump of data from their database. The
data flows into Google Cloud Storage GCS as comma-separated values(CSV) files. You
want to analyze this data in Google BigQuery, but the data could have rows that are
formatted incorrectly or corrupted. How should you build this pipeline? - Answer Run a
Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors
to another dead-letter table for analysis.
Google Cloud Dataflow allows you to create a data pipeline that can preprocess and
transform data before loading it into BigQuery. This approach will enable you to handle
problematic rows, push them to a dead-letter table for later analysis, and load the valid
data into BigQuery.
Your weather app queries a database every 15 minutes to get the current temperature.
The frontend is powered by Google App Engine and server millions of users. How should
you design the frontend to respond to a database failure? - Answer Retry the query with
exponential backoff, up to a cap of 15 minutes.
If your application attempts to connect to the database and does not succeed, the
database could be temporarily unavailable. In this case, sending too many simultaneous
connection requests might waste additional database resources and increase the time
needed to recover. Using exponential backoff prevents your application from sending
,an unresponsive number of connection requests when it can't connect to the database.
You are creating a model to predict housing prices. Due to budget constraints, you must
run it on a single resource-constrained virtual machine. Which learning algorithm should
you use? - Answer Linear regression
To decide when a liner regression should be used or logistics regression needs to be
used: If you are forecasting that is the values in the column that you are predicting is
numeric, it is always liner regression. If you are classifying, that is buy or no buy, yes or
no, you will be using logistics regression.
You are building new real-time data warehouse for your company and will use Google
BigQuery streaming inserts. There is no guarantee that data will only be sent in once but
you do have a unique ID for each row of data and an event timestamp. You want to
ensure that duplicates are not included while interactively querying data. Which query
type should you use? - Answer Use the ROW_NUMBER window function with PARTITION
by unique ID along with WHERE row equals 1.
This approach will assign a row number to each row within a unique ID partition, and by
selecting only rows with a row number of 1, you will ensure that duplicates are excluded
in your query results. It allows you to filter out redundant rows while retaining the latest
or earliest records based on your timestamp column.
Your company is using WILDCARD tables to query data across multiple tables with
similar names. The SQL statement is currently failing with the following error: #Syntax
Error: Expected end of statement but got "-" at [4:11}
SELECT Age FROM bigquery-public-data.noaa_gsod.gsod WHERE age != 99
AND_TABLE_SUFFIX = '1929' ORDER BY age DESC; - Answer
`bigquery-public-data.noaa_gsod.gsod*`
The wildcard table name contains the special character (*), which means that you must
enclose the wildcard table name in backtick (`) characters.
Your company is in a highly regulated industry. One of your requirements is to ensure
individual users have access only to the minimum amount of information required to do
, their jobs. You want to enforce this requirement with Google BigQuery. Which three
approaches can you take? (Choose three.) - Answer 1) Restrict access to tables by role
2) Restrict BigQuery API access to approved users
3) Segregate data across multiple tables or databases
Restrict access to tables by role: You can use BigQuery's access controls to restrict
access to specific tables based on user roles. This allows you to ensure that users can
only access the data they need to do their job. Restrict BigQuery API access to
approved users: By using Cloud Identity and Access Management (IAM) you can control
who has access to the BigQuery API, and what actions they are allowed to perform. This
will help to ensure that only authorized users can access the data. Segregate data
across multiple tables or databases: You can use multiple tables or databases to
separate different types of data, so that users can only access the data they need. This
will prevent users from seeing data they shouldn't have access to.
You are designing a basket abandonment system for an ecommerce company. The
system will send a message to a user based on these rules:
✑ No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
✑ Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be
sent. How should you design the pipeline? - Answer Use a session window with a gap
time duration of 60 minutes
Your company handles data processing for a number of different clients. Each client
prefers to use their own suite of analytics tools, with some allowing direct query access
via Google BigQuery. You need to secure the data so that clients cannot see each
other's data. You want to ensure appropriate access to the data.Which three steps
should you take? (Choose three.) - Answer 1) Load data into a different dataset for each
client.
2) Restrict a client's dataset to approved users.
3) Use the appropriate identity and access management (IAM) roles for each client's
users
